4.2 How AI got here — and the two winters
You can trace AI from Turing to AlphaGo, and say what caused each winter and what ended it.
Before:01. Mathematics for Machine Learning02. Python — Basics to Advanced
Two funding winters shaped this field, and both followed the same arc: a toy demonstration, general promises, then hardware and data that could not deliver. Tracing the line from Turing through the winters to AlphaGo and the ImageNet collapse in error rates is the cheapest available defence against believing the current moment is exempt from the pattern. It sits early in the module because history here is not decoration — it is calibration for every promise the rest of the course will meet.
Work through these
Place the early landmarks: Turing 1950, Dartmouth 1956, ELIZA, SHAKEY
The landmarks that founded the field, from the first serious paper on machine intelligence, through the meeting that named it, to the early conversational and robotic systems. The dates matter less than what each attempt revealed.
Explain the first winter (1974-80): machine translation and the limits of early neural nets
The first collapse in funding came when early results failed to scale, particularly in translation and in the limits of the first neural models. Knowing what was actually promised explains why the disappointment was so sharp.
Explain the second winter (1987-93): LISP machines and expert-system hardware
The second collapse followed the failure of specialised hardware and of the expert systems built on it. It is the clearest example of an approach that worked in demonstrations and not in maintenance.
Connect Deep Blue, Watson and AlphaGo to the ImageNet error-rate collapse that ended the last winter
The chess, quiz and board-game milestones, and the sharp fall in image recognition error that changed the field's fortunes. That last one is what actually ended the second winter.
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